In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data Found celeba Data
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
<matplotlib.image.AxesImage at 0x184c230beb8>
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184d02ac390>
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
<matplotlib.image.AxesImage at 0x184a6ede550>
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.
The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).
You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputsdiscriminatorgeneratormodel_lossmodel_opttrainThis will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
C:\anaconda\envs\tensorflow35\lib\site-packages\ipykernel_launcher.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.
Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width, image_height, and image_channels.z_dim.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
# TODO: Implement Function
real_imgs = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), 'real_imgs')
z_data = tf.placeholder(tf.float32, (None, z_dim), 'z_data')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return real_imgs, z_data, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed
Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
stride = 5
padding = 2
img_filter_size = 64
h2_img_filter_size = img_filter_size*2
h3_img_filter_size = img_filter_size*4
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse=reuse):
# No alpha given so making a test
alpha = 0.2
# 1st Hidden layer
h1 = tf.layers.conv2d(images, img_filter_size, stride, padding, 'same')
h1 = tf.maximum(alpha * h1, h1) # Leaky ReLU
# 2nd Hidden layer
h2 = tf.layers.conv2d(h1, h2_img_filter_size, stride, padding, 'same')
h2 = tf.layers.batch_normalization(h2, training=True) # Batch normalizing
h2 = tf.maximum(alpha * h2, h2) # Leaky ReLU
# 3rd Hidden layer
h3 = tf.layers.conv2d(h2, h3_img_filter_size, stride, padding, 'same')
h3 = tf.layers.batch_normalization(h3, training=True) # Batch normalizing
h3 = tf.maximum(alpha * h3, h3) # Leaky ReLU
flat = tf.reshape(h3, (-1, 4*4*h3_img_filter_size))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed
Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
# TODO: Implement Function
with tf.variable_scope('generator', reuse=not is_train):
# Using the reverse process of the discriminator
alpha = 0.2
h1 = tf.layers.dense(z, 2*2*h3_img_filter_size)
h1 = tf.reshape(h1, (-1, 2, 2, h3_img_filter_size))
h1 = tf.layers.batch_normalization(h1, training=is_train)
h1 = tf.maximum(alpha * h1, h1)
h2 = tf.layers.conv2d_transpose(h1, h2_img_filter_size, 5, 2, 'valid')
h2 = tf.layers.batch_normalization(h2, training=is_train)
h2 = tf.maximum(alpha * h2, h2)
h3 = tf.layers.conv2d_transpose(h2, img_filter_size, 5, 2, 'same')
h3 = tf.layers.batch_normalization(h3, training=is_train)
h3 = tf.maximum(alpha * h3, h3)
logits = tf.layers.conv2d_transpose(h3, out_channel_dim, 5, 2, 'same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed
Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)generator(z, out_channel_dim, is_train=True)def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(input_real)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
# Calculate the Losses for it real, fake & generated models
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(
logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
# The Discriminator Loss is the amount of it fake img loss + it real img loss
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed
Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
# TODO: Implement Function
# Empty TF TrainableVariables
t_vars = tf.trainable_variables()
# Append var to D & G vars arraies if it starts with the "prefix" for on it name
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# discriminator optimization
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)
# UPDATE OPS GraphKeys
ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
g_updates = [opt for opt in ops if opt.name.startswith('generator')]
with tf.control_dependencies(g_updates):
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)model_loss(input_real, input_z, out_channel_dim)model_opt(d_loss, g_loss, learning_rate, beta1)Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
_, img_width, img_height, img_channels = data_shape
real_input_img, z_input, lr = model_inputs(img_width, img_height, img_channels, z_dim)
d_loss, g_loss = model_loss(real_input_img, z_input, img_channels)
d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
steps = 0
losses = []
n_images = 25
print_every = 10
show_every = 100
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# Training the Model
steps += 1 # next batch
batch_images *= 2.0 # the image is increasing by double for each layer
z_sample = np.random.uniform(-1, 1, (batch_size, z_dim)) # uniform distribution
# performing d_opt with the feed_dict knowledge
_ = sess.run(d_opt, feed_dict={real_input_img: batch_images, z_input: z_sample, lr: learning_rate})
# performing g_opt with the feed_dict knowledge
_ = sess.run(g_opt, feed_dict={z_input: z_sample, lr: learning_rate})
# Check if is it time to print the Loss or just keep going (skip the print)
if steps % print_every == 0:
train_loss_d = d_loss.eval({z_input: z_sample, real_input_img: batch_images})
train_loss_g = g_loss.eval({z_input: z_sample})
print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
losses.append((train_loss_d, train_loss_g))
# Showing the generator output based on show_every step
if steps % show_every == 0:
show_generator_output(sess, n_images, z_input, img_channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
z_dim = 100 # The dimension "size" of Z
beta1 = 0.5 # The exponential decay rate for the 1st moment in the optimizer
batch_size = 64 # The size of each amount of samples per processing
learning_rate = 0.002
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.0878... Generator Loss: 6.4049 Epoch 1/2... Discriminator Loss: 0.8140... Generator Loss: 1.2221 Epoch 1/2... Discriminator Loss: 0.9010... Generator Loss: 1.4534 Epoch 1/2... Discriminator Loss: 1.0379... Generator Loss: 0.6374 Epoch 1/2... Discriminator Loss: 0.9390... Generator Loss: 7.6125 Epoch 1/2... Discriminator Loss: 0.6566... Generator Loss: 0.8507 Epoch 1/2... Discriminator Loss: 0.4227... Generator Loss: 1.3187 Epoch 1/2... Discriminator Loss: 0.2860... Generator Loss: 2.0077 Epoch 1/2... Discriminator Loss: 0.7548... Generator Loss: 7.5649 Epoch 1/2... Discriminator Loss: 0.6820... Generator Loss: 2.0793
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b04c5fd0>
Epoch 1/2... Discriminator Loss: 0.4087... Generator Loss: 1.9856 Epoch 1/2... Discriminator Loss: 0.3479... Generator Loss: 2.3912 Epoch 1/2... Discriminator Loss: 1.2545... Generator Loss: 5.9889 Epoch 1/2... Discriminator Loss: 0.3329... Generator Loss: 3.9124 Epoch 1/2... Discriminator Loss: 0.2270... Generator Loss: 3.0585 Epoch 1/2... Discriminator Loss: 1.5462... Generator Loss: 7.1903 Epoch 1/2... Discriminator Loss: 0.1177... Generator Loss: 3.0669 Epoch 1/2... Discriminator Loss: 0.2827... Generator Loss: 2.3052 Epoch 1/2... Discriminator Loss: 0.8654... Generator Loss: 7.0213 Epoch 1/2... Discriminator Loss: 0.2041... Generator Loss: 3.5133
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184afe84fd0>
Epoch 1/2... Discriminator Loss: 0.8154... Generator Loss: 1.4333 Epoch 1/2... Discriminator Loss: 1.4952... Generator Loss: 0.7978 Epoch 1/2... Discriminator Loss: 0.6978... Generator Loss: 3.4511 Epoch 1/2... Discriminator Loss: 0.2147... Generator Loss: 2.3866 Epoch 1/2... Discriminator Loss: 2.0010... Generator Loss: 5.3818 Epoch 1/2... Discriminator Loss: 0.9519... Generator Loss: 0.7696 Epoch 1/2... Discriminator Loss: 1.4153... Generator Loss: 3.0247 Epoch 1/2... Discriminator Loss: 1.5574... Generator Loss: 0.4268 Epoch 1/2... Discriminator Loss: 0.7372... Generator Loss: 1.4979 Epoch 1/2... Discriminator Loss: 0.3407... Generator Loss: 2.4898
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b01d1f28>
Epoch 1/2... Discriminator Loss: 0.2294... Generator Loss: 2.4727 Epoch 1/2... Discriminator Loss: 0.5400... Generator Loss: 1.8783 Epoch 1/2... Discriminator Loss: 0.5913... Generator Loss: 2.1473 Epoch 1/2... Discriminator Loss: 0.4838... Generator Loss: 1.9725 Epoch 1/2... Discriminator Loss: 0.3076... Generator Loss: 3.8957 Epoch 1/2... Discriminator Loss: 1.8004... Generator Loss: 0.5261 Epoch 1/2... Discriminator Loss: 0.7631... Generator Loss: 3.9028 Epoch 1/2... Discriminator Loss: 1.0205... Generator Loss: 1.0967 Epoch 1/2... Discriminator Loss: 0.4534... Generator Loss: 3.2543 Epoch 1/2... Discriminator Loss: 0.6494... Generator Loss: 2.5538
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b02ff400>
Epoch 1/2... Discriminator Loss: 0.8420... Generator Loss: 1.1566 Epoch 1/2... Discriminator Loss: 0.5840... Generator Loss: 1.6012 Epoch 1/2... Discriminator Loss: 0.4351... Generator Loss: 3.0067 Epoch 1/2... Discriminator Loss: 0.8104... Generator Loss: 2.9041 Epoch 1/2... Discriminator Loss: 0.9415... Generator Loss: 2.9508 Epoch 1/2... Discriminator Loss: 0.5187... Generator Loss: 1.6452 Epoch 1/2... Discriminator Loss: 0.4484... Generator Loss: 2.9797 Epoch 1/2... Discriminator Loss: 0.5578... Generator Loss: 1.3598 Epoch 1/2... Discriminator Loss: 0.6338... Generator Loss: 1.6330 Epoch 1/2... Discriminator Loss: 0.6019... Generator Loss: 1.6089
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184aea00630>
Epoch 1/2... Discriminator Loss: 0.8680... Generator Loss: 3.5560 Epoch 1/2... Discriminator Loss: 0.7567... Generator Loss: 1.7517 Epoch 1/2... Discriminator Loss: 0.8223... Generator Loss: 2.5593 Epoch 1/2... Discriminator Loss: 0.6382... Generator Loss: 1.1915 Epoch 1/2... Discriminator Loss: 0.9548... Generator Loss: 1.9197 Epoch 1/2... Discriminator Loss: 0.9977... Generator Loss: 0.8513 Epoch 1/2... Discriminator Loss: 1.4026... Generator Loss: 0.6218 Epoch 1/2... Discriminator Loss: 0.4872... Generator Loss: 2.2225 Epoch 1/2... Discriminator Loss: 0.5256... Generator Loss: 2.2187 Epoch 1/2... Discriminator Loss: 0.6026... Generator Loss: 2.8051
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184afe44860>
Epoch 1/2... Discriminator Loss: 1.0756... Generator Loss: 0.7649 Epoch 1/2... Discriminator Loss: 1.1246... Generator Loss: 2.3075 Epoch 1/2... Discriminator Loss: 0.7546... Generator Loss: 1.1487 Epoch 1/2... Discriminator Loss: 1.0139... Generator Loss: 0.8590 Epoch 1/2... Discriminator Loss: 0.7383... Generator Loss: 1.6263 Epoch 1/2... Discriminator Loss: 0.6776... Generator Loss: 1.3622 Epoch 1/2... Discriminator Loss: 0.8299... Generator Loss: 1.0258 Epoch 1/2... Discriminator Loss: 0.8462... Generator Loss: 1.8495 Epoch 1/2... Discriminator Loss: 0.8211... Generator Loss: 0.9459 Epoch 1/2... Discriminator Loss: 0.6899... Generator Loss: 1.4551
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184afe6b9e8>
Epoch 1/2... Discriminator Loss: 0.8592... Generator Loss: 1.3743 Epoch 1/2... Discriminator Loss: 0.8095... Generator Loss: 1.1433 Epoch 1/2... Discriminator Loss: 0.6694... Generator Loss: 1.3981 Epoch 1/2... Discriminator Loss: 0.9349... Generator Loss: 2.2590 Epoch 1/2... Discriminator Loss: 0.6316... Generator Loss: 1.5789 Epoch 1/2... Discriminator Loss: 0.6009... Generator Loss: 1.5201 Epoch 1/2... Discriminator Loss: 1.4976... Generator Loss: 3.9413 Epoch 1/2... Discriminator Loss: 1.0887... Generator Loss: 0.6276 Epoch 1/2... Discriminator Loss: 0.8576... Generator Loss: 1.4837 Epoch 1/2... Discriminator Loss: 0.8988... Generator Loss: 0.9570
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b04ac400>
Epoch 1/2... Discriminator Loss: 0.9002... Generator Loss: 1.9279 Epoch 1/2... Discriminator Loss: 0.8510... Generator Loss: 1.1800 Epoch 1/2... Discriminator Loss: 0.7461... Generator Loss: 1.2205 Epoch 1/2... Discriminator Loss: 0.7205... Generator Loss: 1.9045 Epoch 1/2... Discriminator Loss: 0.8104... Generator Loss: 1.3649 Epoch 1/2... Discriminator Loss: 2.0668... Generator Loss: 0.4173 Epoch 1/2... Discriminator Loss: 1.3659... Generator Loss: 1.6760 Epoch 1/2... Discriminator Loss: 0.6980... Generator Loss: 1.0990 Epoch 1/2... Discriminator Loss: 0.8428... Generator Loss: 1.1100 Epoch 1/2... Discriminator Loss: 0.6168... Generator Loss: 1.6976
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b05cedd8>
Epoch 1/2... Discriminator Loss: 0.7194... Generator Loss: 1.3726 Epoch 1/2... Discriminator Loss: 0.8000... Generator Loss: 1.0593 Epoch 1/2... Discriminator Loss: 1.1139... Generator Loss: 1.9102 Epoch 2/2... Discriminator Loss: 1.0598... Generator Loss: 0.6805 Epoch 2/2... Discriminator Loss: 0.9375... Generator Loss: 0.9797 Epoch 2/2... Discriminator Loss: 0.9582... Generator Loss: 0.8774 Epoch 2/2... Discriminator Loss: 0.9614... Generator Loss: 1.4882 Epoch 2/2... Discriminator Loss: 1.2075... Generator Loss: 0.5537 Epoch 2/2... Discriminator Loss: 0.9594... Generator Loss: 1.4356 Epoch 2/2... Discriminator Loss: 0.8547... Generator Loss: 0.9771
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b271c860>
Epoch 2/2... Discriminator Loss: 0.8619... Generator Loss: 1.2122 Epoch 2/2... Discriminator Loss: 0.7970... Generator Loss: 1.7451 Epoch 2/2... Discriminator Loss: 1.1037... Generator Loss: 0.5781 Epoch 2/2... Discriminator Loss: 1.3343... Generator Loss: 0.4878 Epoch 2/2... Discriminator Loss: 0.7843... Generator Loss: 1.7188 Epoch 2/2... Discriminator Loss: 0.8445... Generator Loss: 1.1709 Epoch 2/2... Discriminator Loss: 1.3181... Generator Loss: 0.4917 Epoch 2/2... Discriminator Loss: 0.9177... Generator Loss: 0.9260 Epoch 2/2... Discriminator Loss: 0.8380... Generator Loss: 1.6159 Epoch 2/2... Discriminator Loss: 1.3926... Generator Loss: 0.4114
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b2fb1f60>
Epoch 2/2... Discriminator Loss: 0.9365... Generator Loss: 0.9060 Epoch 2/2... Discriminator Loss: 0.7178... Generator Loss: 1.2817 Epoch 2/2... Discriminator Loss: 0.8220... Generator Loss: 0.9024 Epoch 2/2... Discriminator Loss: 0.7632... Generator Loss: 1.0008 Epoch 2/2... Discriminator Loss: 0.8517... Generator Loss: 0.8919 Epoch 2/2... Discriminator Loss: 1.0458... Generator Loss: 0.9854 Epoch 2/2... Discriminator Loss: 0.7524... Generator Loss: 1.0311 Epoch 2/2... Discriminator Loss: 1.3389... Generator Loss: 0.4477 Epoch 2/2... Discriminator Loss: 0.7105... Generator Loss: 1.2043 Epoch 2/2... Discriminator Loss: 0.5779... Generator Loss: 1.5825
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184aea18780>
Epoch 2/2... Discriminator Loss: 0.6515... Generator Loss: 2.5954 Epoch 2/2... Discriminator Loss: 0.9390... Generator Loss: 1.9135 Epoch 2/2... Discriminator Loss: 0.8633... Generator Loss: 1.0343 Epoch 2/2... Discriminator Loss: 1.1027... Generator Loss: 0.6121 Epoch 2/2... Discriminator Loss: 1.0978... Generator Loss: 2.2571 Epoch 2/2... Discriminator Loss: 0.9354... Generator Loss: 0.7581 Epoch 2/2... Discriminator Loss: 0.9538... Generator Loss: 0.7458 Epoch 2/2... Discriminator Loss: 1.1162... Generator Loss: 0.6849 Epoch 2/2... Discriminator Loss: 1.2499... Generator Loss: 1.3440 Epoch 2/2... Discriminator Loss: 1.3150... Generator Loss: 2.5719
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b30485f8>
Epoch 2/2... Discriminator Loss: 1.1888... Generator Loss: 0.5832 Epoch 2/2... Discriminator Loss: 1.3189... Generator Loss: 0.4681 Epoch 2/2... Discriminator Loss: 0.7344... Generator Loss: 2.8116 Epoch 2/2... Discriminator Loss: 0.7582... Generator Loss: 1.4002 Epoch 2/2... Discriminator Loss: 1.2632... Generator Loss: 0.5511 Epoch 2/2... Discriminator Loss: 0.8839... Generator Loss: 0.7819 Epoch 2/2... Discriminator Loss: 4.1812... Generator Loss: 5.6805 Epoch 2/2... Discriminator Loss: 0.9225... Generator Loss: 0.8735 Epoch 2/2... Discriminator Loss: 0.7582... Generator Loss: 1.3553 Epoch 2/2... Discriminator Loss: 0.9742... Generator Loss: 1.7083
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b3118f60>
Epoch 2/2... Discriminator Loss: 0.9876... Generator Loss: 1.6263 Epoch 2/2... Discriminator Loss: 1.5367... Generator Loss: 2.5411 Epoch 2/2... Discriminator Loss: 1.5508... Generator Loss: 0.3440 Epoch 2/2... Discriminator Loss: 0.5765... Generator Loss: 1.4743 Epoch 2/2... Discriminator Loss: 1.0020... Generator Loss: 0.7016 Epoch 2/2... Discriminator Loss: 0.5756... Generator Loss: 1.4107 Epoch 2/2... Discriminator Loss: 0.6167... Generator Loss: 2.7777 Epoch 2/2... Discriminator Loss: 0.9740... Generator Loss: 1.4134 Epoch 2/2... Discriminator Loss: 0.5547... Generator Loss: 1.3186 Epoch 2/2... Discriminator Loss: 1.0417... Generator Loss: 0.7029
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b322c198>
Epoch 2/2... Discriminator Loss: 0.8512... Generator Loss: 0.9155 Epoch 2/2... Discriminator Loss: 1.0253... Generator Loss: 0.6216 Epoch 2/2... Discriminator Loss: 0.4966... Generator Loss: 1.5247 Epoch 2/2... Discriminator Loss: 0.7536... Generator Loss: 1.0321 Epoch 2/2... Discriminator Loss: 0.5480... Generator Loss: 1.5126 Epoch 2/2... Discriminator Loss: 1.0581... Generator Loss: 0.6394 Epoch 2/2... Discriminator Loss: 1.1952... Generator Loss: 0.6554 Epoch 2/2... Discriminator Loss: 0.6418... Generator Loss: 1.4931 Epoch 2/2... Discriminator Loss: 1.5287... Generator Loss: 0.4017 Epoch 2/2... Discriminator Loss: 1.1726... Generator Loss: 0.6790
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b6ce1dd8>
Epoch 2/2... Discriminator Loss: 1.1961... Generator Loss: 0.5601 Epoch 2/2... Discriminator Loss: 0.6264... Generator Loss: 1.5202 Epoch 2/2... Discriminator Loss: 2.2659... Generator Loss: 0.2142 Epoch 2/2... Discriminator Loss: 2.0370... Generator Loss: 0.2691 Epoch 2/2... Discriminator Loss: 1.0805... Generator Loss: 1.3773 Epoch 2/2... Discriminator Loss: 0.7992... Generator Loss: 1.0141 Epoch 2/2... Discriminator Loss: 1.0252... Generator Loss: 0.6793 Epoch 2/2... Discriminator Loss: 0.8838... Generator Loss: 3.3364 Epoch 2/2... Discriminator Loss: 1.1569... Generator Loss: 0.5924 Epoch 2/2... Discriminator Loss: 1.4040... Generator Loss: 0.3804
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b6dcc828>
Epoch 2/2... Discriminator Loss: 2.2077... Generator Loss: 0.2449 Epoch 2/2... Discriminator Loss: 1.4084... Generator Loss: 0.4648 Epoch 2/2... Discriminator Loss: 1.6599... Generator Loss: 0.3569 Epoch 2/2... Discriminator Loss: 0.8015... Generator Loss: 1.2287 Epoch 2/2... Discriminator Loss: 0.6837... Generator Loss: 1.4772 Epoch 2/2... Discriminator Loss: 0.6988... Generator Loss: 1.7289 Epoch 2/2... Discriminator Loss: 0.7287... Generator Loss: 1.5039 Epoch 2/2... Discriminator Loss: 0.9768... Generator Loss: 0.8264 Epoch 2/2... Discriminator Loss: 0.4878... Generator Loss: 1.3537 Epoch 2/2... Discriminator Loss: 0.8912... Generator Loss: 0.9999
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\formatters.py in __call__(self, obj) 330 pass 331 else: --> 332 return printer(obj) 333 # Finally look for special method names 334 method = get_real_method(obj, self.print_method) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig) 235 236 if 'png' in formats: --> 237 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs)) 238 if 'retina' in formats or 'png2x' in formats: 239 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs)) C:\anaconda\envs\tensorflow35\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs) 119 120 bytes_io = BytesIO() --> 121 fig.canvas.print_figure(bytes_io, **kw) 122 data = bytes_io.getvalue() 123 if fmt == 'svg': C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs) 2206 orientation=orientation, 2207 dryrun=True, -> 2208 **kwargs) 2209 renderer = self.figure._cachedRenderer 2210 bbox_inches = self.figure.get_tightbbox(renderer) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs) 505 506 def print_png(self, filename_or_obj, *args, **kwargs): --> 507 FigureCanvasAgg.draw(self) 508 renderer = self.get_renderer() 509 original_dpi = renderer.dpi C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self) 428 if toolbar: 429 toolbar.set_cursor(cursors.WAIT) --> 430 self.figure.draw(self.renderer) 431 finally: 432 if toolbar: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\figure.py in draw(self, renderer) 1293 1294 mimage._draw_list_compositing_images( -> 1295 renderer, self, artists, self.suppressComposite) 1296 1297 renderer.close_group('figure') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe) 2397 renderer.stop_rasterizing() 2398 -> 2399 mimage._draw_list_compositing_images(renderer, self, artists) 2400 2401 renderer.close_group('axes') C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite) 136 if not_composite or not has_images: 137 for a in artists: --> 138 a.draw(renderer) 139 else: 140 # Composite any adjacent images together C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs) 53 renderer.start_filter() 54 ---> 55 return draw(artist, renderer, *args, **kwargs) 56 finally: 57 if artist.get_agg_filter() is not None: C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs) 546 else: 547 im, l, b, trans = self.make_image( --> 548 renderer, renderer.get_image_magnification()) 549 if im is not None: 550 renderer.draw_image(gc, l, b, im) C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled) 772 return self._make_image( 773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification, --> 774 unsampled=unsampled) 775 776 def _check_unsampled_image(self, renderer): C:\anaconda\envs\tensorflow35\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border) 410 411 mask = np.empty(A.shape, dtype=np.float32) --> 412 if A.mask.shape == A.shape: 413 # this is the case of a nontrivial mask 414 mask[:] = np.where(A.mask, np.float32(np.nan), AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x184b8ec2550>
Epoch 2/2... Discriminator Loss: 1.0861... Generator Loss: 0.8647 Epoch 2/2... Discriminator Loss: 1.4254... Generator Loss: 0.4662 Epoch 2/2... Discriminator Loss: 0.7079... Generator Loss: 1.2455 Epoch 2/2... Discriminator Loss: 1.2594... Generator Loss: 0.6100 Epoch 2/2... Discriminator Loss: 1.4714... Generator Loss: 0.4359 Epoch 2/2... Discriminator Loss: 0.8124... Generator Loss: 1.1852 Epoch 2/2... Discriminator Loss: 0.7163... Generator Loss: 1.3574
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
z_dim = z_dim
beta1 = beta1
batch_size = batch_size
learning_rate = learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 0.5409... Generator Loss: 6.6437 Epoch 1/2... Discriminator Loss: 1.3252... Generator Loss: 7.3785 Epoch 1/2... Discriminator Loss: 0.5452... Generator Loss: 1.6278 Epoch 1/2... Discriminator Loss: 0.2217... Generator Loss: 2.9998 Epoch 1/2... Discriminator Loss: 0.8415... Generator Loss: 4.6753 Epoch 1/2... Discriminator Loss: 0.9318... Generator Loss: 0.6426 Epoch 1/2... Discriminator Loss: 0.2589... Generator Loss: 3.0743 Epoch 1/2... Discriminator Loss: 1.7129... Generator Loss: 0.4234 Epoch 1/2... Discriminator Loss: 1.5826... Generator Loss: 4.9026 Epoch 1/2... Discriminator Loss: 1.2950... Generator Loss: 0.7303
Epoch 1/2... Discriminator Loss: 1.0192... Generator Loss: 0.5862 Epoch 1/2... Discriminator Loss: 0.6343... Generator Loss: 1.0039 Epoch 1/2... Discriminator Loss: 0.2072... Generator Loss: 2.1789 Epoch 1/2... Discriminator Loss: 3.1371... Generator Loss: 0.4011 Epoch 1/2... Discriminator Loss: 0.8817... Generator Loss: 2.8640 Epoch 1/2... Discriminator Loss: 1.0612... Generator Loss: 0.7206 Epoch 1/2... Discriminator Loss: 0.5329... Generator Loss: 1.3586 Epoch 1/2... Discriminator Loss: 0.3840... Generator Loss: 2.2172 Epoch 1/2... Discriminator Loss: 1.7202... Generator Loss: 1.3848 Epoch 1/2... Discriminator Loss: 0.6483... Generator Loss: 1.3996
Epoch 1/2... Discriminator Loss: 1.3753... Generator Loss: 1.1062 Epoch 1/2... Discriminator Loss: 0.8372... Generator Loss: 0.7834 Epoch 1/2... Discriminator Loss: 0.4149... Generator Loss: 1.4623 Epoch 1/2... Discriminator Loss: 0.3662... Generator Loss: 3.1293 Epoch 1/2... Discriminator Loss: 0.7620... Generator Loss: 2.2448 Epoch 1/2... Discriminator Loss: 3.0357... Generator Loss: 5.4549 Epoch 1/2... Discriminator Loss: 0.5821... Generator Loss: 1.2989 Epoch 1/2... Discriminator Loss: 1.1218... Generator Loss: 0.9870 Epoch 1/2... Discriminator Loss: 1.2923... Generator Loss: 1.7192 Epoch 1/2... Discriminator Loss: 0.9818... Generator Loss: 1.1606
Epoch 1/2... Discriminator Loss: 1.0629... Generator Loss: 3.1470 Epoch 1/2... Discriminator Loss: 3.8036... Generator Loss: 5.1409 Epoch 1/2... Discriminator Loss: 1.1319... Generator Loss: 0.7115 Epoch 1/2... Discriminator Loss: 0.9516... Generator Loss: 1.2629 Epoch 1/2... Discriminator Loss: 1.1288... Generator Loss: 0.6257 Epoch 1/2... Discriminator Loss: 1.0338... Generator Loss: 0.8374 Epoch 1/2... Discriminator Loss: 1.1571... Generator Loss: 0.9486 Epoch 1/2... Discriminator Loss: 1.0799... Generator Loss: 0.6960 Epoch 1/2... Discriminator Loss: 1.0433... Generator Loss: 0.6873 Epoch 1/2... Discriminator Loss: 1.2760... Generator Loss: 0.5042
Epoch 1/2... Discriminator Loss: 0.9199... Generator Loss: 1.9849 Epoch 1/2... Discriminator Loss: 1.1746... Generator Loss: 0.5873 Epoch 1/2... Discriminator Loss: 1.4513... Generator Loss: 2.4069 Epoch 1/2... Discriminator Loss: 1.2395... Generator Loss: 0.5632 Epoch 1/2... Discriminator Loss: 1.0477... Generator Loss: 0.6615 Epoch 1/2... Discriminator Loss: 0.7669... Generator Loss: 1.0555 Epoch 1/2... Discriminator Loss: 1.0309... Generator Loss: 0.6652 Epoch 1/2... Discriminator Loss: 1.5680... Generator Loss: 0.3431 Epoch 1/2... Discriminator Loss: 0.6636... Generator Loss: 1.3595 Epoch 1/2... Discriminator Loss: 2.5038... Generator Loss: 3.2951
Epoch 1/2... Discriminator Loss: 1.3835... Generator Loss: 0.5137 Epoch 1/2... Discriminator Loss: 1.5656... Generator Loss: 0.3781 Epoch 1/2... Discriminator Loss: 0.6111... Generator Loss: 1.3024 Epoch 1/2... Discriminator Loss: 0.6167... Generator Loss: 1.2104 Epoch 1/2... Discriminator Loss: 1.4702... Generator Loss: 0.5422 Epoch 1/2... Discriminator Loss: 0.9288... Generator Loss: 1.1823 Epoch 1/2... Discriminator Loss: 0.8821... Generator Loss: 1.7834 Epoch 1/2... Discriminator Loss: 1.0103... Generator Loss: 2.5550 Epoch 1/2... Discriminator Loss: 1.2324... Generator Loss: 2.7544 Epoch 1/2... Discriminator Loss: 1.4156... Generator Loss: 0.4468
Epoch 1/2... Discriminator Loss: 1.1114... Generator Loss: 0.6631 Epoch 1/2... Discriminator Loss: 0.9133... Generator Loss: 0.9225 Epoch 1/2... Discriminator Loss: 0.7469... Generator Loss: 2.0840 Epoch 1/2... Discriminator Loss: 1.9063... Generator Loss: 3.5590 Epoch 1/2... Discriminator Loss: 0.9908... Generator Loss: 0.9781 Epoch 1/2... Discriminator Loss: 0.8639... Generator Loss: 1.1782 Epoch 1/2... Discriminator Loss: 0.8045... Generator Loss: 1.2929 Epoch 1/2... Discriminator Loss: 0.8037... Generator Loss: 1.2021 Epoch 1/2... Discriminator Loss: 1.8171... Generator Loss: 0.2723 Epoch 1/2... Discriminator Loss: 1.5314... Generator Loss: 0.4016
Epoch 1/2... Discriminator Loss: 1.6685... Generator Loss: 2.6423 Epoch 1/2... Discriminator Loss: 1.5163... Generator Loss: 0.4920 Epoch 1/2... Discriminator Loss: 1.5247... Generator Loss: 3.4482 Epoch 1/2... Discriminator Loss: 0.6517... Generator Loss: 1.2485 Epoch 1/2... Discriminator Loss: 0.7217... Generator Loss: 1.3765 Epoch 1/2... Discriminator Loss: 0.8033... Generator Loss: 0.9621 Epoch 1/2... Discriminator Loss: 2.2713... Generator Loss: 0.1535 Epoch 1/2... Discriminator Loss: 0.6873... Generator Loss: 1.5382 Epoch 1/2... Discriminator Loss: 1.0817... Generator Loss: 0.7517 Epoch 1/2... Discriminator Loss: 1.1375... Generator Loss: 0.6480
Epoch 1/2... Discriminator Loss: 0.8254... Generator Loss: 1.2566 Epoch 1/2... Discriminator Loss: 0.9945... Generator Loss: 0.9732 Epoch 1/2... Discriminator Loss: 1.2296... Generator Loss: 0.5966 Epoch 1/2... Discriminator Loss: 1.2268... Generator Loss: 0.7802 Epoch 1/2... Discriminator Loss: 1.1435... Generator Loss: 0.5699 Epoch 1/2... Discriminator Loss: 1.0861... Generator Loss: 0.6385 Epoch 1/2... Discriminator Loss: 0.7214... Generator Loss: 1.4172 Epoch 1/2... Discriminator Loss: 0.8062... Generator Loss: 1.4388 Epoch 1/2... Discriminator Loss: 1.3215... Generator Loss: 0.5128 Epoch 1/2... Discriminator Loss: 0.7705... Generator Loss: 1.1615
Epoch 1/2... Discriminator Loss: 1.1346... Generator Loss: 0.8681 Epoch 1/2... Discriminator Loss: 1.0953... Generator Loss: 0.7579 Epoch 1/2... Discriminator Loss: 0.9095... Generator Loss: 0.9349 Epoch 1/2... Discriminator Loss: 0.9948... Generator Loss: 1.5497 Epoch 1/2... Discriminator Loss: 1.0282... Generator Loss: 1.9258 Epoch 1/2... Discriminator Loss: 1.3438... Generator Loss: 0.4900 Epoch 1/2... Discriminator Loss: 1.3134... Generator Loss: 0.4605 Epoch 1/2... Discriminator Loss: 0.8145... Generator Loss: 1.1744 Epoch 1/2... Discriminator Loss: 1.0510... Generator Loss: 0.6364 Epoch 1/2... Discriminator Loss: 1.1008... Generator Loss: 0.6483
Epoch 1/2... Discriminator Loss: 1.1222... Generator Loss: 0.6461 Epoch 1/2... Discriminator Loss: 0.8812... Generator Loss: 1.0910 Epoch 1/2... Discriminator Loss: 1.0112... Generator Loss: 0.6872 Epoch 1/2... Discriminator Loss: 0.8022... Generator Loss: 0.9040 Epoch 1/2... Discriminator Loss: 1.5136... Generator Loss: 3.0799 Epoch 1/2... Discriminator Loss: 1.1973... Generator Loss: 1.7603 Epoch 1/2... Discriminator Loss: 0.8717... Generator Loss: 1.1959 Epoch 1/2... Discriminator Loss: 0.9480... Generator Loss: 0.7884 Epoch 1/2... Discriminator Loss: 1.0632... Generator Loss: 0.6442 Epoch 1/2... Discriminator Loss: 1.0560... Generator Loss: 0.7619
Epoch 1/2... Discriminator Loss: 1.2660... Generator Loss: 0.5192 Epoch 1/2... Discriminator Loss: 0.8619... Generator Loss: 1.6845 Epoch 1/2... Discriminator Loss: 0.8992... Generator Loss: 1.2291 Epoch 1/2... Discriminator Loss: 0.6723... Generator Loss: 1.5050 Epoch 1/2... Discriminator Loss: 0.8884... Generator Loss: 1.9251 Epoch 1/2... Discriminator Loss: 1.0393... Generator Loss: 0.7061 Epoch 1/2... Discriminator Loss: 1.5036... Generator Loss: 0.3993 Epoch 1/2... Discriminator Loss: 0.8288... Generator Loss: 1.0063 Epoch 1/2... Discriminator Loss: 0.9719... Generator Loss: 0.7070 Epoch 1/2... Discriminator Loss: 0.6271... Generator Loss: 2.0092
Epoch 1/2... Discriminator Loss: 0.7980... Generator Loss: 1.3880 Epoch 1/2... Discriminator Loss: 1.2002... Generator Loss: 0.6571 Epoch 1/2... Discriminator Loss: 1.3081... Generator Loss: 0.4664 Epoch 1/2... Discriminator Loss: 2.2893... Generator Loss: 0.1569 Epoch 1/2... Discriminator Loss: 0.8956... Generator Loss: 1.0097 Epoch 1/2... Discriminator Loss: 1.4204... Generator Loss: 0.4211 Epoch 1/2... Discriminator Loss: 1.0818... Generator Loss: 0.6538 Epoch 1/2... Discriminator Loss: 1.4192... Generator Loss: 0.4252 Epoch 1/2... Discriminator Loss: 1.5436... Generator Loss: 0.3987 Epoch 1/2... Discriminator Loss: 0.8884... Generator Loss: 0.8997
Epoch 1/2... Discriminator Loss: 1.1499... Generator Loss: 1.2081 Epoch 1/2... Discriminator Loss: 1.1716... Generator Loss: 2.0261 Epoch 1/2... Discriminator Loss: 0.8966... Generator Loss: 1.9576 Epoch 1/2... Discriminator Loss: 0.6668... Generator Loss: 1.3947 Epoch 1/2... Discriminator Loss: 0.8538... Generator Loss: 1.4476 Epoch 1/2... Discriminator Loss: 0.8491... Generator Loss: 1.4375 Epoch 1/2... Discriminator Loss: 1.2812... Generator Loss: 0.4637 Epoch 1/2... Discriminator Loss: 0.6554... Generator Loss: 1.3952 Epoch 1/2... Discriminator Loss: 1.0917... Generator Loss: 0.8797 Epoch 1/2... Discriminator Loss: 0.8002... Generator Loss: 1.5682
Epoch 1/2... Discriminator Loss: 0.7952... Generator Loss: 1.1532 Epoch 1/2... Discriminator Loss: 0.9604... Generator Loss: 1.8066 Epoch 1/2... Discriminator Loss: 0.9169... Generator Loss: 0.8563 Epoch 1/2... Discriminator Loss: 0.7829... Generator Loss: 1.7234 Epoch 1/2... Discriminator Loss: 0.9406... Generator Loss: 1.3436 Epoch 1/2... Discriminator Loss: 0.8714... Generator Loss: 0.9492 Epoch 1/2... Discriminator Loss: 0.8760... Generator Loss: 2.2077 Epoch 1/2... Discriminator Loss: 1.3171... Generator Loss: 0.5053 Epoch 1/2... Discriminator Loss: 0.9774... Generator Loss: 0.9464 Epoch 1/2... Discriminator Loss: 0.7493... Generator Loss: 0.8930
Epoch 1/2... Discriminator Loss: 0.8505... Generator Loss: 1.2217 Epoch 1/2... Discriminator Loss: 0.9118... Generator Loss: 0.8704 Epoch 1/2... Discriminator Loss: 1.1546... Generator Loss: 2.4503 Epoch 1/2... Discriminator Loss: 0.9884... Generator Loss: 1.8560 Epoch 1/2... Discriminator Loss: 0.8999... Generator Loss: 0.9959 Epoch 1/2... Discriminator Loss: 0.9723... Generator Loss: 1.0551 Epoch 1/2... Discriminator Loss: 1.5778... Generator Loss: 0.3187 Epoch 1/2... Discriminator Loss: 1.2795... Generator Loss: 0.5236 Epoch 1/2... Discriminator Loss: 1.5660... Generator Loss: 0.3183 Epoch 1/2... Discriminator Loss: 0.9461... Generator Loss: 1.5847
Epoch 1/2... Discriminator Loss: 0.7916... Generator Loss: 1.1905 Epoch 1/2... Discriminator Loss: 1.2304... Generator Loss: 0.5533 Epoch 1/2... Discriminator Loss: 1.5422... Generator Loss: 0.4001 Epoch 1/2... Discriminator Loss: 1.5503... Generator Loss: 0.3282 Epoch 1/2... Discriminator Loss: 1.0590... Generator Loss: 0.6826 Epoch 1/2... Discriminator Loss: 0.9327... Generator Loss: 1.2819 Epoch 1/2... Discriminator Loss: 1.0095... Generator Loss: 1.0497 Epoch 1/2... Discriminator Loss: 1.1129... Generator Loss: 1.9062 Epoch 1/2... Discriminator Loss: 1.2200... Generator Loss: 2.7099 Epoch 1/2... Discriminator Loss: 1.0800... Generator Loss: 0.6092
Epoch 1/2... Discriminator Loss: 0.7243... Generator Loss: 1.6017 Epoch 1/2... Discriminator Loss: 0.7642... Generator Loss: 1.1996 Epoch 1/2... Discriminator Loss: 0.8371... Generator Loss: 1.0958 Epoch 1/2... Discriminator Loss: 0.8574... Generator Loss: 1.2256 Epoch 1/2... Discriminator Loss: 0.9005... Generator Loss: 2.3894 Epoch 1/2... Discriminator Loss: 0.9286... Generator Loss: 0.8672 Epoch 1/2... Discriminator Loss: 0.9773... Generator Loss: 0.9861 Epoch 1/2... Discriminator Loss: 0.8509... Generator Loss: 1.1330 Epoch 1/2... Discriminator Loss: 1.7411... Generator Loss: 0.3181 Epoch 1/2... Discriminator Loss: 0.9792... Generator Loss: 1.3514
Epoch 1/2... Discriminator Loss: 1.3343... Generator Loss: 2.3484 Epoch 1/2... Discriminator Loss: 1.8632... Generator Loss: 0.3169 Epoch 1/2... Discriminator Loss: 1.0162... Generator Loss: 1.0207 Epoch 1/2... Discriminator Loss: 1.4533... Generator Loss: 2.8359 Epoch 1/2... Discriminator Loss: 1.2684... Generator Loss: 1.0188 Epoch 1/2... Discriminator Loss: 0.8176... Generator Loss: 1.5109 Epoch 1/2... Discriminator Loss: 1.2550... Generator Loss: 1.2602 Epoch 1/2... Discriminator Loss: 1.5420... Generator Loss: 0.4192 Epoch 1/2... Discriminator Loss: 1.2948... Generator Loss: 2.0722 Epoch 1/2... Discriminator Loss: 0.7777... Generator Loss: 1.6294
Epoch 1/2... Discriminator Loss: 0.8492... Generator Loss: 1.3100 Epoch 1/2... Discriminator Loss: 0.9973... Generator Loss: 1.1620 Epoch 1/2... Discriminator Loss: 1.1264... Generator Loss: 0.7434 Epoch 1/2... Discriminator Loss: 0.9706... Generator Loss: 1.7476 Epoch 1/2... Discriminator Loss: 1.7533... Generator Loss: 0.2898 Epoch 1/2... Discriminator Loss: 0.9377... Generator Loss: 0.9859 Epoch 1/2... Discriminator Loss: 1.1076... Generator Loss: 0.7925 Epoch 1/2... Discriminator Loss: 0.7932... Generator Loss: 1.0267 Epoch 1/2... Discriminator Loss: 1.0437... Generator Loss: 0.7182 Epoch 1/2... Discriminator Loss: 0.8554... Generator Loss: 1.2175
Epoch 1/2... Discriminator Loss: 1.2471... Generator Loss: 0.4967 Epoch 1/2... Discriminator Loss: 0.9413... Generator Loss: 0.8803 Epoch 1/2... Discriminator Loss: 0.9638... Generator Loss: 1.8242 Epoch 1/2... Discriminator Loss: 0.8446... Generator Loss: 1.2666 Epoch 1/2... Discriminator Loss: 1.1401... Generator Loss: 0.7300 Epoch 1/2... Discriminator Loss: 0.8217... Generator Loss: 1.1300 Epoch 1/2... Discriminator Loss: 1.1566... Generator Loss: 0.6142 Epoch 1/2... Discriminator Loss: 1.0057... Generator Loss: 2.0666 Epoch 1/2... Discriminator Loss: 1.2433... Generator Loss: 0.6143 Epoch 1/2... Discriminator Loss: 0.9825... Generator Loss: 1.5707
Epoch 1/2... Discriminator Loss: 0.8501... Generator Loss: 1.0076 Epoch 1/2... Discriminator Loss: 0.8096... Generator Loss: 0.9971 Epoch 1/2... Discriminator Loss: 0.8551... Generator Loss: 1.1541 Epoch 1/2... Discriminator Loss: 1.0339... Generator Loss: 0.6856 Epoch 1/2... Discriminator Loss: 0.9083... Generator Loss: 0.8864 Epoch 1/2... Discriminator Loss: 1.0311... Generator Loss: 0.9233 Epoch 1/2... Discriminator Loss: 0.8980... Generator Loss: 0.9380 Epoch 1/2... Discriminator Loss: 0.9603... Generator Loss: 0.7475 Epoch 1/2... Discriminator Loss: 1.2356... Generator Loss: 0.4953 Epoch 1/2... Discriminator Loss: 0.7602... Generator Loss: 1.2115
Epoch 1/2... Discriminator Loss: 1.1979... Generator Loss: 1.3356 Epoch 1/2... Discriminator Loss: 0.9705... Generator Loss: 0.7194 Epoch 1/2... Discriminator Loss: 1.0598... Generator Loss: 1.1599 Epoch 1/2... Discriminator Loss: 1.0383... Generator Loss: 1.6423 Epoch 1/2... Discriminator Loss: 1.1266... Generator Loss: 0.6663 Epoch 1/2... Discriminator Loss: 1.0772... Generator Loss: 0.7562 Epoch 1/2... Discriminator Loss: 1.2381... Generator Loss: 0.5287 Epoch 1/2... Discriminator Loss: 0.8748... Generator Loss: 1.0915 Epoch 1/2... Discriminator Loss: 0.8819... Generator Loss: 1.8235 Epoch 1/2... Discriminator Loss: 0.7216... Generator Loss: 2.0442
Epoch 1/2... Discriminator Loss: 0.9513... Generator Loss: 0.7937 Epoch 1/2... Discriminator Loss: 0.8870... Generator Loss: 1.3033 Epoch 1/2... Discriminator Loss: 1.0615... Generator Loss: 0.7495 Epoch 1/2... Discriminator Loss: 1.0135... Generator Loss: 1.1600 Epoch 1/2... Discriminator Loss: 1.5745... Generator Loss: 2.6511 Epoch 1/2... Discriminator Loss: 0.8999... Generator Loss: 1.5119 Epoch 1/2... Discriminator Loss: 0.9640... Generator Loss: 0.7429 Epoch 1/2... Discriminator Loss: 0.8164... Generator Loss: 1.1688 Epoch 1/2... Discriminator Loss: 0.8226... Generator Loss: 1.0588 Epoch 1/2... Discriminator Loss: 0.9760... Generator Loss: 0.7054
Epoch 1/2... Discriminator Loss: 1.0172... Generator Loss: 1.6686 Epoch 1/2... Discriminator Loss: 1.7474... Generator Loss: 0.3059 Epoch 1/2... Discriminator Loss: 0.9063... Generator Loss: 1.2824 Epoch 1/2... Discriminator Loss: 1.0161... Generator Loss: 1.5682 Epoch 1/2... Discriminator Loss: 1.0899... Generator Loss: 0.7396 Epoch 1/2... Discriminator Loss: 1.3197... Generator Loss: 0.4448 Epoch 1/2... Discriminator Loss: 1.1248... Generator Loss: 0.8425 Epoch 1/2... Discriminator Loss: 0.8899... Generator Loss: 1.4715 Epoch 1/2... Discriminator Loss: 0.8199... Generator Loss: 1.2880 Epoch 1/2... Discriminator Loss: 1.3646... Generator Loss: 0.3888
Epoch 1/2... Discriminator Loss: 1.0560... Generator Loss: 1.0421 Epoch 1/2... Discriminator Loss: 1.2271... Generator Loss: 0.5711 Epoch 1/2... Discriminator Loss: 1.2141... Generator Loss: 0.5350 Epoch 1/2... Discriminator Loss: 0.8297... Generator Loss: 1.0629 Epoch 1/2... Discriminator Loss: 1.6399... Generator Loss: 0.3529 Epoch 1/2... Discriminator Loss: 0.9308... Generator Loss: 1.3568 Epoch 1/2... Discriminator Loss: 1.8791... Generator Loss: 2.8605 Epoch 1/2... Discriminator Loss: 0.8080... Generator Loss: 1.6674 Epoch 1/2... Discriminator Loss: 0.9073... Generator Loss: 1.4589 Epoch 1/2... Discriminator Loss: 0.9080... Generator Loss: 0.9473
Epoch 1/2... Discriminator Loss: 0.7966... Generator Loss: 1.3206 Epoch 1/2... Discriminator Loss: 0.9160... Generator Loss: 1.5095 Epoch 1/2... Discriminator Loss: 1.3182... Generator Loss: 0.4632 Epoch 1/2... Discriminator Loss: 1.1335... Generator Loss: 0.7641 Epoch 1/2... Discriminator Loss: 0.9248... Generator Loss: 0.7212 Epoch 1/2... Discriminator Loss: 1.2051... Generator Loss: 0.8354 Epoch 1/2... Discriminator Loss: 1.1722... Generator Loss: 0.5584 Epoch 1/2... Discriminator Loss: 0.8272... Generator Loss: 0.9735 Epoch 1/2... Discriminator Loss: 0.8676... Generator Loss: 1.3329 Epoch 1/2... Discriminator Loss: 0.9340... Generator Loss: 0.9141
Epoch 1/2... Discriminator Loss: 1.2780... Generator Loss: 1.0564 Epoch 1/2... Discriminator Loss: 1.1769... Generator Loss: 0.5986 Epoch 1/2... Discriminator Loss: 1.1394... Generator Loss: 0.6412 Epoch 1/2... Discriminator Loss: 0.8403... Generator Loss: 1.2667 Epoch 1/2... Discriminator Loss: 1.0662... Generator Loss: 0.8106 Epoch 1/2... Discriminator Loss: 0.8338... Generator Loss: 0.9542 Epoch 1/2... Discriminator Loss: 0.9038... Generator Loss: 1.2786 Epoch 1/2... Discriminator Loss: 1.0325... Generator Loss: 0.8791 Epoch 1/2... Discriminator Loss: 1.3137... Generator Loss: 0.4661 Epoch 1/2... Discriminator Loss: 0.8733... Generator Loss: 1.2247
Epoch 1/2... Discriminator Loss: 0.9738... Generator Loss: 0.8204 Epoch 1/2... Discriminator Loss: 1.1917... Generator Loss: 0.5253 Epoch 1/2... Discriminator Loss: 0.8532... Generator Loss: 1.2266 Epoch 1/2... Discriminator Loss: 1.1540... Generator Loss: 1.5037 Epoch 1/2... Discriminator Loss: 1.1713... Generator Loss: 2.1004 Epoch 1/2... Discriminator Loss: 1.2865... Generator Loss: 0.4750 Epoch 1/2... Discriminator Loss: 1.1190... Generator Loss: 1.3883 Epoch 1/2... Discriminator Loss: 1.1198... Generator Loss: 0.7113 Epoch 1/2... Discriminator Loss: 0.8888... Generator Loss: 1.7378 Epoch 1/2... Discriminator Loss: 1.1022... Generator Loss: 0.6319
Epoch 1/2... Discriminator Loss: 1.7436... Generator Loss: 2.3015 Epoch 1/2... Discriminator Loss: 1.1219... Generator Loss: 1.6702 Epoch 1/2... Discriminator Loss: 0.9333... Generator Loss: 1.4144 Epoch 1/2... Discriminator Loss: 1.4135... Generator Loss: 1.7749 Epoch 1/2... Discriminator Loss: 1.1050... Generator Loss: 1.0279 Epoch 1/2... Discriminator Loss: 0.8351... Generator Loss: 0.9664 Epoch 1/2... Discriminator Loss: 0.9270... Generator Loss: 0.8445 Epoch 1/2... Discriminator Loss: 0.9956... Generator Loss: 0.7900 Epoch 1/2... Discriminator Loss: 1.3118... Generator Loss: 0.5905 Epoch 1/2... Discriminator Loss: 1.1803... Generator Loss: 0.5335
Epoch 1/2... Discriminator Loss: 0.9339... Generator Loss: 1.2881 Epoch 1/2... Discriminator Loss: 0.9915... Generator Loss: 0.8416 Epoch 1/2... Discriminator Loss: 1.0677... Generator Loss: 1.6856 Epoch 1/2... Discriminator Loss: 1.0564... Generator Loss: 1.4888 Epoch 1/2... Discriminator Loss: 1.1285... Generator Loss: 0.6048 Epoch 1/2... Discriminator Loss: 1.0384... Generator Loss: 0.8584 Epoch 1/2... Discriminator Loss: 1.1795... Generator Loss: 1.8580 Epoch 1/2... Discriminator Loss: 0.8244... Generator Loss: 1.1070 Epoch 1/2... Discriminator Loss: 1.2321... Generator Loss: 0.6267 Epoch 1/2... Discriminator Loss: 1.0202... Generator Loss: 0.6713
Epoch 1/2... Discriminator Loss: 1.1626... Generator Loss: 0.6813 Epoch 1/2... Discriminator Loss: 0.8802... Generator Loss: 0.8409 Epoch 1/2... Discriminator Loss: 0.8833... Generator Loss: 1.4348 Epoch 1/2... Discriminator Loss: 0.8565... Generator Loss: 0.9023 Epoch 1/2... Discriminator Loss: 1.1473... Generator Loss: 1.1558 Epoch 1/2... Discriminator Loss: 1.1468... Generator Loss: 0.5587 Epoch 2/2... Discriminator Loss: 0.9554... Generator Loss: 1.0037 Epoch 2/2... Discriminator Loss: 0.8500... Generator Loss: 1.2180 Epoch 2/2... Discriminator Loss: 0.9340... Generator Loss: 1.8060 Epoch 2/2... Discriminator Loss: 0.7958... Generator Loss: 1.5531
Epoch 2/2... Discriminator Loss: 0.7602... Generator Loss: 1.1542 Epoch 2/2... Discriminator Loss: 0.9914... Generator Loss: 1.5271 Epoch 2/2... Discriminator Loss: 0.9960... Generator Loss: 0.7048 Epoch 2/2... Discriminator Loss: 1.8187... Generator Loss: 2.5485 Epoch 2/2... Discriminator Loss: 0.9196... Generator Loss: 0.8580 Epoch 2/2... Discriminator Loss: 0.9557... Generator Loss: 0.7960 Epoch 2/2... Discriminator Loss: 1.0492... Generator Loss: 0.8807 Epoch 2/2... Discriminator Loss: 0.7259... Generator Loss: 1.3422 Epoch 2/2... Discriminator Loss: 0.8760... Generator Loss: 1.4400 Epoch 2/2... Discriminator Loss: 1.3156... Generator Loss: 0.4834
Epoch 2/2... Discriminator Loss: 0.8293... Generator Loss: 1.2571 Epoch 2/2... Discriminator Loss: 1.0477... Generator Loss: 1.6304 Epoch 2/2... Discriminator Loss: 1.0447... Generator Loss: 0.6895 Epoch 2/2... Discriminator Loss: 0.9741... Generator Loss: 0.8731 Epoch 2/2... Discriminator Loss: 0.9231... Generator Loss: 1.3255 Epoch 2/2... Discriminator Loss: 0.8855... Generator Loss: 0.8801 Epoch 2/2... Discriminator Loss: 1.0255... Generator Loss: 0.9919 Epoch 2/2... Discriminator Loss: 1.0998... Generator Loss: 1.8780 Epoch 2/2... Discriminator Loss: 0.9569... Generator Loss: 0.9021 Epoch 2/2... Discriminator Loss: 0.9758... Generator Loss: 0.9149
Epoch 2/2... Discriminator Loss: 0.9361... Generator Loss: 0.8656 Epoch 2/2... Discriminator Loss: 0.6776... Generator Loss: 1.6457 Epoch 2/2... Discriminator Loss: 1.0256... Generator Loss: 0.6372 Epoch 2/2... Discriminator Loss: 0.9541... Generator Loss: 0.8094 Epoch 2/2... Discriminator Loss: 0.8599... Generator Loss: 1.4443 Epoch 2/2... Discriminator Loss: 1.0261... Generator Loss: 1.1969 Epoch 2/2... Discriminator Loss: 1.0160... Generator Loss: 0.9741 Epoch 2/2... Discriminator Loss: 0.9280... Generator Loss: 1.5277 Epoch 2/2... Discriminator Loss: 0.9889... Generator Loss: 1.0894 Epoch 2/2... Discriminator Loss: 0.8589... Generator Loss: 1.0872
Epoch 2/2... Discriminator Loss: 0.8562... Generator Loss: 1.1459 Epoch 2/2... Discriminator Loss: 1.1258... Generator Loss: 0.6595 Epoch 2/2... Discriminator Loss: 1.4462... Generator Loss: 0.4182 Epoch 2/2... Discriminator Loss: 0.8731... Generator Loss: 0.9766 Epoch 2/2... Discriminator Loss: 0.9591... Generator Loss: 0.8459 Epoch 2/2... Discriminator Loss: 1.4997... Generator Loss: 0.3454 Epoch 2/2... Discriminator Loss: 1.0584... Generator Loss: 1.0292 Epoch 2/2... Discriminator Loss: 1.4780... Generator Loss: 0.3902 Epoch 2/2... Discriminator Loss: 0.9818... Generator Loss: 0.7278 Epoch 2/2... Discriminator Loss: 1.1698... Generator Loss: 1.7170
Epoch 2/2... Discriminator Loss: 0.7294... Generator Loss: 1.2364 Epoch 2/2... Discriminator Loss: 1.4923... Generator Loss: 0.3981 Epoch 2/2... Discriminator Loss: 1.0969... Generator Loss: 0.6591 Epoch 2/2... Discriminator Loss: 0.9022... Generator Loss: 1.0334 Epoch 2/2... Discriminator Loss: 0.9805... Generator Loss: 0.7678 Epoch 2/2... Discriminator Loss: 1.0674... Generator Loss: 0.8350 Epoch 2/2... Discriminator Loss: 0.9700... Generator Loss: 1.2178 Epoch 2/2... Discriminator Loss: 0.8188... Generator Loss: 1.1139 Epoch 2/2... Discriminator Loss: 1.0575... Generator Loss: 0.7029 Epoch 2/2... Discriminator Loss: 1.0084... Generator Loss: 0.7642
Epoch 2/2... Discriminator Loss: 0.9794... Generator Loss: 0.8529 Epoch 2/2... Discriminator Loss: 1.0900... Generator Loss: 0.9418 Epoch 2/2... Discriminator Loss: 0.8707... Generator Loss: 1.2890 Epoch 2/2... Discriminator Loss: 1.0952... Generator Loss: 1.3102 Epoch 2/2... Discriminator Loss: 1.3143... Generator Loss: 0.4177 Epoch 2/2... Discriminator Loss: 0.9542... Generator Loss: 0.8699 Epoch 2/2... Discriminator Loss: 1.0399... Generator Loss: 1.9968 Epoch 2/2... Discriminator Loss: 1.2267... Generator Loss: 0.5681 Epoch 2/2... Discriminator Loss: 0.7539... Generator Loss: 1.1559 Epoch 2/2... Discriminator Loss: 0.9195... Generator Loss: 1.1068
Epoch 2/2... Discriminator Loss: 0.9844... Generator Loss: 1.7162 Epoch 2/2... Discriminator Loss: 1.1362... Generator Loss: 1.5968 Epoch 2/2... Discriminator Loss: 1.1078... Generator Loss: 0.6727 Epoch 2/2... Discriminator Loss: 1.2393... Generator Loss: 0.4819 Epoch 2/2... Discriminator Loss: 1.0625... Generator Loss: 0.8275 Epoch 2/2... Discriminator Loss: 1.0001... Generator Loss: 1.1212 Epoch 2/2... Discriminator Loss: 0.7516... Generator Loss: 1.4588 Epoch 2/2... Discriminator Loss: 0.8011... Generator Loss: 1.2037 Epoch 2/2... Discriminator Loss: 0.9590... Generator Loss: 1.3211 Epoch 2/2... Discriminator Loss: 0.9725... Generator Loss: 0.8947
Epoch 2/2... Discriminator Loss: 1.0054... Generator Loss: 1.6919 Epoch 2/2... Discriminator Loss: 1.3174... Generator Loss: 0.4239 Epoch 2/2... Discriminator Loss: 1.1252... Generator Loss: 0.6504 Epoch 2/2... Discriminator Loss: 1.0121... Generator Loss: 1.3644 Epoch 2/2... Discriminator Loss: 1.1764... Generator Loss: 0.6185 Epoch 2/2... Discriminator Loss: 1.3268... Generator Loss: 0.4654 Epoch 2/2... Discriminator Loss: 1.1929... Generator Loss: 0.7158 Epoch 2/2... Discriminator Loss: 1.1773... Generator Loss: 1.6005 Epoch 2/2... Discriminator Loss: 0.9399... Generator Loss: 0.9589 Epoch 2/2... Discriminator Loss: 0.9216... Generator Loss: 1.1445
Epoch 2/2... Discriminator Loss: 1.0166... Generator Loss: 0.8077 Epoch 2/2... Discriminator Loss: 1.0601... Generator Loss: 1.1054 Epoch 2/2... Discriminator Loss: 0.8482... Generator Loss: 1.2797 Epoch 2/2... Discriminator Loss: 0.8410... Generator Loss: 1.3247 Epoch 2/2... Discriminator Loss: 1.0972... Generator Loss: 0.8645 Epoch 2/2... Discriminator Loss: 1.8285... Generator Loss: 0.2588 Epoch 2/2... Discriminator Loss: 0.8610... Generator Loss: 1.1032 Epoch 2/2... Discriminator Loss: 0.8363... Generator Loss: 1.3466 Epoch 2/2... Discriminator Loss: 0.8685... Generator Loss: 1.0533 Epoch 2/2... Discriminator Loss: 0.7969... Generator Loss: 2.0164
Epoch 2/2... Discriminator Loss: 0.9740... Generator Loss: 1.1717 Epoch 2/2... Discriminator Loss: 1.0269... Generator Loss: 1.0528 Epoch 2/2... Discriminator Loss: 0.8995... Generator Loss: 0.9872 Epoch 2/2... Discriminator Loss: 0.9428... Generator Loss: 1.5934 Epoch 2/2... Discriminator Loss: 0.8520... Generator Loss: 1.0104 Epoch 2/2... Discriminator Loss: 1.0349... Generator Loss: 1.8824 Epoch 2/2... Discriminator Loss: 0.9848... Generator Loss: 0.8182 Epoch 2/2... Discriminator Loss: 0.9745... Generator Loss: 0.9401 Epoch 2/2... Discriminator Loss: 1.1141... Generator Loss: 0.6782 Epoch 2/2... Discriminator Loss: 1.0163... Generator Loss: 0.7361
Epoch 2/2... Discriminator Loss: 0.9836... Generator Loss: 0.8000 Epoch 2/2... Discriminator Loss: 0.8565... Generator Loss: 1.0011 Epoch 2/2... Discriminator Loss: 1.1740... Generator Loss: 0.6598 Epoch 2/2... Discriminator Loss: 1.0586... Generator Loss: 0.7923 Epoch 2/2... Discriminator Loss: 1.0284... Generator Loss: 0.9395 Epoch 2/2... Discriminator Loss: 1.2845... Generator Loss: 0.5050 Epoch 2/2... Discriminator Loss: 0.7676... Generator Loss: 1.5598 Epoch 2/2... Discriminator Loss: 1.2139... Generator Loss: 0.5530 Epoch 2/2... Discriminator Loss: 1.2530... Generator Loss: 0.4958 Epoch 2/2... Discriminator Loss: 1.5958... Generator Loss: 0.3475
Epoch 2/2... Discriminator Loss: 0.8751... Generator Loss: 1.2557 Epoch 2/2... Discriminator Loss: 1.2363... Generator Loss: 0.5123 Epoch 2/2... Discriminator Loss: 1.2239... Generator Loss: 0.4988 Epoch 2/2... Discriminator Loss: 0.8786... Generator Loss: 1.3118 Epoch 2/2... Discriminator Loss: 1.1703... Generator Loss: 0.5864 Epoch 2/2... Discriminator Loss: 0.9467... Generator Loss: 1.2672 Epoch 2/2... Discriminator Loss: 0.8567... Generator Loss: 0.8485 Epoch 2/2... Discriminator Loss: 1.1847... Generator Loss: 2.3693 Epoch 2/2... Discriminator Loss: 1.2243... Generator Loss: 0.5736 Epoch 2/2... Discriminator Loss: 0.8764... Generator Loss: 1.0936
Epoch 2/2... Discriminator Loss: 0.9756... Generator Loss: 1.3525 Epoch 2/2... Discriminator Loss: 1.0258... Generator Loss: 0.8748 Epoch 2/2... Discriminator Loss: 0.9442... Generator Loss: 1.1115 Epoch 2/2... Discriminator Loss: 1.2615... Generator Loss: 0.4719 Epoch 2/2... Discriminator Loss: 1.0259... Generator Loss: 0.8180 Epoch 2/2... Discriminator Loss: 1.1623... Generator Loss: 0.6156 Epoch 2/2... Discriminator Loss: 1.1882... Generator Loss: 1.8294 Epoch 2/2... Discriminator Loss: 0.8726... Generator Loss: 0.9487 Epoch 2/2... Discriminator Loss: 1.0774... Generator Loss: 0.8206 Epoch 2/2... Discriminator Loss: 0.9185... Generator Loss: 0.7958
Epoch 2/2... Discriminator Loss: 0.8838... Generator Loss: 1.0933 Epoch 2/2... Discriminator Loss: 1.0096... Generator Loss: 1.9564 Epoch 2/2... Discriminator Loss: 1.3739... Generator Loss: 1.9207 Epoch 2/2... Discriminator Loss: 1.0098... Generator Loss: 1.4364 Epoch 2/2... Discriminator Loss: 0.8808... Generator Loss: 1.1157 Epoch 2/2... Discriminator Loss: 1.0173... Generator Loss: 0.8390 Epoch 2/2... Discriminator Loss: 1.6870... Generator Loss: 0.2912 Epoch 2/2... Discriminator Loss: 1.2063... Generator Loss: 0.5099 Epoch 2/2... Discriminator Loss: 1.5325... Generator Loss: 0.3552 Epoch 2/2... Discriminator Loss: 1.6656... Generator Loss: 0.3255
Epoch 2/2... Discriminator Loss: 1.0401... Generator Loss: 0.7183 Epoch 2/2... Discriminator Loss: 1.1165... Generator Loss: 0.6887 Epoch 2/2... Discriminator Loss: 0.8967... Generator Loss: 0.9055 Epoch 2/2... Discriminator Loss: 1.0181... Generator Loss: 0.7260 Epoch 2/2... Discriminator Loss: 0.9517... Generator Loss: 0.7031 Epoch 2/2... Discriminator Loss: 0.8921... Generator Loss: 0.8677 Epoch 2/2... Discriminator Loss: 1.0835... Generator Loss: 2.1984 Epoch 2/2... Discriminator Loss: 0.9926... Generator Loss: 0.7205 Epoch 2/2... Discriminator Loss: 0.8247... Generator Loss: 1.2616 Epoch 2/2... Discriminator Loss: 1.0517... Generator Loss: 0.6925
Epoch 2/2... Discriminator Loss: 1.0317... Generator Loss: 0.8000 Epoch 2/2... Discriminator Loss: 1.2089... Generator Loss: 0.5663 Epoch 2/2... Discriminator Loss: 1.2505... Generator Loss: 0.4544 Epoch 2/2... Discriminator Loss: 0.8515... Generator Loss: 0.8845 Epoch 2/2... Discriminator Loss: 1.5541... Generator Loss: 0.3391 Epoch 2/2... Discriminator Loss: 0.9091... Generator Loss: 1.0124 Epoch 2/2... Discriminator Loss: 0.9271... Generator Loss: 1.1190 Epoch 2/2... Discriminator Loss: 0.7301... Generator Loss: 1.5002 Epoch 2/2... Discriminator Loss: 0.8798... Generator Loss: 0.9651 Epoch 2/2... Discriminator Loss: 1.2695... Generator Loss: 0.4996
Epoch 2/2... Discriminator Loss: 1.0883... Generator Loss: 0.8821 Epoch 2/2... Discriminator Loss: 0.8065... Generator Loss: 1.3352 Epoch 2/2... Discriminator Loss: 1.3466... Generator Loss: 0.5535 Epoch 2/2... Discriminator Loss: 0.9586... Generator Loss: 0.9208 Epoch 2/2... Discriminator Loss: 0.9403... Generator Loss: 0.8404 Epoch 2/2... Discriminator Loss: 1.0426... Generator Loss: 0.7441 Epoch 2/2... Discriminator Loss: 1.3954... Generator Loss: 0.3845 Epoch 2/2... Discriminator Loss: 2.1940... Generator Loss: 0.1945 Epoch 2/2... Discriminator Loss: 0.8907... Generator Loss: 1.1257 Epoch 2/2... Discriminator Loss: 0.9905... Generator Loss: 1.2095
Epoch 2/2... Discriminator Loss: 0.7787... Generator Loss: 1.1018 Epoch 2/2... Discriminator Loss: 1.1360... Generator Loss: 1.7956 Epoch 2/2... Discriminator Loss: 0.9851... Generator Loss: 1.6061 Epoch 2/2... Discriminator Loss: 1.3106... Generator Loss: 1.3505 Epoch 2/2... Discriminator Loss: 1.0336... Generator Loss: 0.7688 Epoch 2/2... Discriminator Loss: 0.8137... Generator Loss: 1.6604 Epoch 2/2... Discriminator Loss: 0.8985... Generator Loss: 1.1034 Epoch 2/2... Discriminator Loss: 0.8887... Generator Loss: 2.0674 Epoch 2/2... Discriminator Loss: 0.8057... Generator Loss: 0.8913 Epoch 2/2... Discriminator Loss: 1.1291... Generator Loss: 1.2954
Epoch 2/2... Discriminator Loss: 0.9269... Generator Loss: 0.9307 Epoch 2/2... Discriminator Loss: 0.8952... Generator Loss: 1.7367 Epoch 2/2... Discriminator Loss: 1.0887... Generator Loss: 0.6678 Epoch 2/2... Discriminator Loss: 0.9755... Generator Loss: 0.7218 Epoch 2/2... Discriminator Loss: 0.8181... Generator Loss: 1.3878 Epoch 2/2... Discriminator Loss: 1.1833... Generator Loss: 1.9362 Epoch 2/2... Discriminator Loss: 1.5601... Generator Loss: 0.3210 Epoch 2/2... Discriminator Loss: 1.2117... Generator Loss: 0.7490 Epoch 2/2... Discriminator Loss: 0.9444... Generator Loss: 0.8133 Epoch 2/2... Discriminator Loss: 1.1132... Generator Loss: 1.4763
Epoch 2/2... Discriminator Loss: 0.7823... Generator Loss: 1.0060 Epoch 2/2... Discriminator Loss: 1.0236... Generator Loss: 0.9481 Epoch 2/2... Discriminator Loss: 0.9932... Generator Loss: 1.1546 Epoch 2/2... Discriminator Loss: 1.0435... Generator Loss: 0.7248 Epoch 2/2... Discriminator Loss: 1.0879... Generator Loss: 1.4447 Epoch 2/2... Discriminator Loss: 1.3607... Generator Loss: 0.3940 Epoch 2/2... Discriminator Loss: 1.0105... Generator Loss: 0.8571 Epoch 2/2... Discriminator Loss: 1.0578... Generator Loss: 0.7367 Epoch 2/2... Discriminator Loss: 0.9926... Generator Loss: 1.5372 Epoch 2/2... Discriminator Loss: 1.1143... Generator Loss: 1.2059
Epoch 2/2... Discriminator Loss: 0.9335... Generator Loss: 0.9258 Epoch 2/2... Discriminator Loss: 1.0710... Generator Loss: 0.5881 Epoch 2/2... Discriminator Loss: 1.1650... Generator Loss: 0.5888 Epoch 2/2... Discriminator Loss: 0.9432... Generator Loss: 1.8909 Epoch 2/2... Discriminator Loss: 0.8688... Generator Loss: 1.0766 Epoch 2/2... Discriminator Loss: 1.5645... Generator Loss: 1.2139 Epoch 2/2... Discriminator Loss: 0.9547... Generator Loss: 0.8808 Epoch 2/2... Discriminator Loss: 1.0199... Generator Loss: 1.3081 Epoch 2/2... Discriminator Loss: 0.9709... Generator Loss: 0.7863 Epoch 2/2... Discriminator Loss: 1.0388... Generator Loss: 0.7121
Epoch 2/2... Discriminator Loss: 0.7690... Generator Loss: 1.8098 Epoch 2/2... Discriminator Loss: 0.8055... Generator Loss: 1.3425 Epoch 2/2... Discriminator Loss: 1.3186... Generator Loss: 0.5220 Epoch 2/2... Discriminator Loss: 1.0929... Generator Loss: 1.9118 Epoch 2/2... Discriminator Loss: 0.9484... Generator Loss: 2.3309 Epoch 2/2... Discriminator Loss: 0.9102... Generator Loss: 1.7193 Epoch 2/2... Discriminator Loss: 0.8858... Generator Loss: 0.8696 Epoch 2/2... Discriminator Loss: 1.5643... Generator Loss: 0.4427 Epoch 2/2... Discriminator Loss: 0.9932... Generator Loss: 1.5309 Epoch 2/2... Discriminator Loss: 0.8784... Generator Loss: 0.8961
Epoch 2/2... Discriminator Loss: 0.8433... Generator Loss: 0.9850 Epoch 2/2... Discriminator Loss: 1.0274... Generator Loss: 1.6175 Epoch 2/2... Discriminator Loss: 0.8316... Generator Loss: 2.1480 Epoch 2/2... Discriminator Loss: 1.1443... Generator Loss: 0.7239 Epoch 2/2... Discriminator Loss: 0.8479... Generator Loss: 1.1544 Epoch 2/2... Discriminator Loss: 1.1206... Generator Loss: 0.8050 Epoch 2/2... Discriminator Loss: 0.8932... Generator Loss: 1.0489 Epoch 2/2... Discriminator Loss: 0.8109... Generator Loss: 1.0159 Epoch 2/2... Discriminator Loss: 0.9921... Generator Loss: 0.7077 Epoch 2/2... Discriminator Loss: 0.8531... Generator Loss: 1.2814
Epoch 2/2... Discriminator Loss: 0.9544... Generator Loss: 1.4194 Epoch 2/2... Discriminator Loss: 1.1046... Generator Loss: 0.8608 Epoch 2/2... Discriminator Loss: 0.7637... Generator Loss: 1.6504 Epoch 2/2... Discriminator Loss: 1.0438... Generator Loss: 1.0360 Epoch 2/2... Discriminator Loss: 1.0798... Generator Loss: 1.3173 Epoch 2/2... Discriminator Loss: 0.8770... Generator Loss: 1.4163 Epoch 2/2... Discriminator Loss: 1.2207... Generator Loss: 0.8162 Epoch 2/2... Discriminator Loss: 1.1039... Generator Loss: 0.6665 Epoch 2/2... Discriminator Loss: 0.7834... Generator Loss: 1.1533 Epoch 2/2... Discriminator Loss: 0.9878... Generator Loss: 1.1364
Epoch 2/2... Discriminator Loss: 1.1495... Generator Loss: 2.0229 Epoch 2/2... Discriminator Loss: 0.7973... Generator Loss: 1.0895 Epoch 2/2... Discriminator Loss: 0.8930... Generator Loss: 1.4103 Epoch 2/2... Discriminator Loss: 1.6780... Generator Loss: 0.2626 Epoch 2/2... Discriminator Loss: 0.7894... Generator Loss: 1.1823 Epoch 2/2... Discriminator Loss: 0.8641... Generator Loss: 1.0484 Epoch 2/2... Discriminator Loss: 1.4989... Generator Loss: 0.3446 Epoch 2/2... Discriminator Loss: 1.0705... Generator Loss: 0.5975 Epoch 2/2... Discriminator Loss: 0.8372... Generator Loss: 1.0757 Epoch 2/2... Discriminator Loss: 0.8591... Generator Loss: 1.2597
Epoch 2/2... Discriminator Loss: 1.1370... Generator Loss: 0.6083 Epoch 2/2... Discriminator Loss: 1.1838... Generator Loss: 1.5299 Epoch 2/2... Discriminator Loss: 1.0480... Generator Loss: 1.2846 Epoch 2/2... Discriminator Loss: 1.0673... Generator Loss: 1.2394 Epoch 2/2... Discriminator Loss: 1.6615... Generator Loss: 2.5018 Epoch 2/2... Discriminator Loss: 1.2247... Generator Loss: 1.6460 Epoch 2/2... Discriminator Loss: 0.7667... Generator Loss: 1.2234 Epoch 2/2... Discriminator Loss: 0.8795... Generator Loss: 0.8792 Epoch 2/2... Discriminator Loss: 1.2132... Generator Loss: 0.5615 Epoch 2/2... Discriminator Loss: 0.9486... Generator Loss: 0.7987
Epoch 2/2... Discriminator Loss: 1.0880... Generator Loss: 2.0940 Epoch 2/2... Discriminator Loss: 0.9972... Generator Loss: 0.9893 Epoch 2/2... Discriminator Loss: 1.1881... Generator Loss: 0.5426 Epoch 2/2... Discriminator Loss: 0.8834... Generator Loss: 1.8256 Epoch 2/2... Discriminator Loss: 0.9633... Generator Loss: 0.7805 Epoch 2/2... Discriminator Loss: 0.6537... Generator Loss: 1.3289 Epoch 2/2... Discriminator Loss: 1.1996... Generator Loss: 0.7190 Epoch 2/2... Discriminator Loss: 0.7163... Generator Loss: 1.4612 Epoch 2/2... Discriminator Loss: 0.9932... Generator Loss: 1.0446 Epoch 2/2... Discriminator Loss: 1.1881... Generator Loss: 0.5216
Epoch 2/2... Discriminator Loss: 0.8718... Generator Loss: 1.2204 Epoch 2/2... Discriminator Loss: 0.8539... Generator Loss: 1.1403 Epoch 2/2... Discriminator Loss: 2.0352... Generator Loss: 0.1961 Epoch 2/2... Discriminator Loss: 1.1660... Generator Loss: 0.5544 Epoch 2/2... Discriminator Loss: 0.7620... Generator Loss: 1.9947 Epoch 2/2... Discriminator Loss: 1.1167... Generator Loss: 0.6176 Epoch 2/2... Discriminator Loss: 0.8548... Generator Loss: 0.9276 Epoch 2/2... Discriminator Loss: 1.4279... Generator Loss: 0.4094 Epoch 2/2... Discriminator Loss: 0.8810... Generator Loss: 1.7429 Epoch 2/2... Discriminator Loss: 0.9728... Generator Loss: 0.8677
Epoch 2/2... Discriminator Loss: 0.8404... Generator Loss: 1.8055 Epoch 2/2... Discriminator Loss: 1.1865... Generator Loss: 1.7001 Epoch 2/2... Discriminator Loss: 0.6253... Generator Loss: 1.5393 Epoch 2/2... Discriminator Loss: 0.9302... Generator Loss: 1.5092 Epoch 2/2... Discriminator Loss: 0.8750... Generator Loss: 2.1169 Epoch 2/2... Discriminator Loss: 0.5404... Generator Loss: 1.4915 Epoch 2/2... Discriminator Loss: 0.8954... Generator Loss: 0.9341 Epoch 2/2... Discriminator Loss: 0.9825... Generator Loss: 1.2022 Epoch 2/2... Discriminator Loss: 0.9263... Generator Loss: 0.9625 Epoch 2/2... Discriminator Loss: 0.9535... Generator Loss: 0.9133
Epoch 2/2... Discriminator Loss: 1.1959... Generator Loss: 0.5854 Epoch 2/2... Discriminator Loss: 0.8541... Generator Loss: 1.3051 Epoch 2/2... Discriminator Loss: 0.8972... Generator Loss: 1.3233 Epoch 2/2... Discriminator Loss: 0.8116... Generator Loss: 1.2846 Epoch 2/2... Discriminator Loss: 1.0865... Generator Loss: 0.5895 Epoch 2/2... Discriminator Loss: 1.3852... Generator Loss: 1.8468 Epoch 2/2... Discriminator Loss: 0.6964... Generator Loss: 1.2092 Epoch 2/2... Discriminator Loss: 1.4335... Generator Loss: 2.1714 Epoch 2/2... Discriminator Loss: 0.9266... Generator Loss: 1.0530 Epoch 2/2... Discriminator Loss: 0.7880... Generator Loss: 1.1058
Epoch 2/2... Discriminator Loss: 0.8894... Generator Loss: 1.0283 Epoch 2/2... Discriminator Loss: 1.1862... Generator Loss: 0.5506 Epoch 2/2... Discriminator Loss: 1.2624... Generator Loss: 0.4805 Epoch 2/2... Discriminator Loss: 1.0648... Generator Loss: 1.7616 Epoch 2/2... Discriminator Loss: 1.0090... Generator Loss: 1.0718 Epoch 2/2... Discriminator Loss: 0.8591... Generator Loss: 0.8784 Epoch 2/2... Discriminator Loss: 0.9740... Generator Loss: 2.0067 Epoch 2/2... Discriminator Loss: 1.1199... Generator Loss: 0.7387 Epoch 2/2... Discriminator Loss: 0.9490... Generator Loss: 1.0840 Epoch 2/2... Discriminator Loss: 1.0015... Generator Loss: 1.9927
Epoch 2/2... Discriminator Loss: 0.9113... Generator Loss: 1.2068 Epoch 2/2... Discriminator Loss: 0.7219... Generator Loss: 1.1360 Epoch 2/2... Discriminator Loss: 1.2626... Generator Loss: 0.5494
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.